Enterprise financial risk early warning and risk detection identification method based on DTW-RNN

Abstract

The invention relates to the technical field of big data analysis, in particular to an enterprise financial risk early warning and risk detection and identification method based on DTW-RNN, comprising the following steps: S1, firstly obtaining a to-be-detected enterprise list; the method comprises the steps of S1, analyzing a financial statement of a corresponding enterprise through financial ratio analysis, S3, paying attention to volatility and abnormal conditions of a financial market, S4, performing wider analysis on a data source through an artificial intelligence mode, and S5, performing a corresponding pressure test. The purpose of enterprise financial risk early warning is to timely discover and deal with potential financial risks, the enterprise can better deal with various financial risks by establishing and perfecting the financial risk early warning system, the long-term development of the enterprise is guaranteed, and the economic benefits of the enterprise are improved. The financial risk of the enterprise can be analyzed according to the financial ratio to help the enterprise to know the financial condition, the operation result and the cash flow condition, so that possible risk points are found, and the risk is identified, detected and analyzed in advance to facilitate timely adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis. Specifically, it relates to a method for enterprise financial risk early warning and risk detection and identification based on DTW-RNN. Background Art

[0002] With the development of social economy, computer technology and mobile Internet technology have entered a period of rapid development. All walks of life have started to move towards integration with the Internet. In particular, relevant departments have provided a large number of convenient services, enabling people to handle various businesses without leaving home. Along with these systems, a huge amount of data has been generated. A large amount of information is contained in these huge amounts of data, which has rich utilization value. By analyzing these data, it can help relevant departments uncover potential risk hazards. The existing methods for early warning of financial enterprise operation risks are mainly used for the supervision of financial enterprises by government supervision departments, which can classify the financial risk levels of enterprises, realize the priority classification of the supervision of financial enterprises, and improve the supervision efficiency. Correctly analyzing and predicting the operation risks of financial market entities, accurately warning of risks, and quickly taking measures and making decisions are effective ways to avoid financial risks. Establishing an early warning model for the operation risks of financial enterprises and adhering to using data to speak have great significance for guiding the pertinence and timeliness of relevant departments' law enforcement and maintaining social stability.

[0003] Internet finance, by virtue of Internet technology, has risks that are more concealed, contagious, sudden, and extensive compared with traditional finance. In recent years, network financial risk events have shown an explosive growth. Along with the emergence of risks, the current Internet finance itself has poor early warning capabilities and does not have the ability to identify and detect risks, and cannot effectively determine the types and levels of risks. The existing traditional enterprise financial risk monitoring solutions mainly have the following problems: on the one hand, relying on whistleblower reports, the discovery of risks lags behind, and whistleblower reports often only appear after the risks have broken out; on the other hand, the determination of enterprise financial risks mostly relies on manual case-by-case judgments, lacking objectivity and having low judgment efficiency. There is a need for a method for enterprise financial risk early warning and risk detection and identification based on DTW-RNN to solve this problem. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for enterprise financial risk early warning and risk detection and identification based on DTW-RNN to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for enterprise financial risk early warning and risk detection and identification based on DTW-RNN, including the following steps:

[0006] S1. First, obtain the list of enterprises to be detected; for each enterprise to be detected in the enterprise list, extract the characteristic attribute data of the current enterprise to be detected.

[0007] S2. Then, analyze the financial statements of the corresponding enterprises through financial ratio analysis, such as the balance sheet, income statement, and cash flow statement, to identify potential financial risks.

[0008] S3. Secondly, pay attention to the volatility and anomalies in the financial markets, including the stock market, bond market, and foreign exchange market, and also pay attention to the macroeconomic environment risks of a country or region, such as inflation, exchange rate fluctuations, and market liquidity risks. It is also necessary to understand the risk status of financial institutions and check the financial, liquidity, credit, and operational risks that enterprises may face.

[0009] S4. Through artificial intelligence, conduct a more extensive analysis of the data sources, establish a more accurate customer credit model. Through machine learning algorithms, financial institutions can better predict the customer default risk and automatically adjust the credit limit to make an accurate credit risk assessment.

[0010] S5. And conduct corresponding stress tests to simulate extremely adverse economic environments or market conditions and evaluate the debt repayment ability and default possibility of enterprises under these circumstances.

[0011] S6. Finally, conduct corresponding risk classification for the enterprises, and classify different levels for different enterprise risks for reference.

[0012] As a preferred technical solution of the present invention, in step S1, the enterprise list can conduct statistics on all enterprises according to relevant specifications, which is convenient for statistical induction of previous-year statements, and then extract corresponding statements for different enterprises for comparison.

[0013] As a preferred technical solution of the present invention, the financial ratio analysis in step S2 can help enterprises understand their financial status, operating results, and cash flow conditions, so as to discover possible risk points.

[0014] As a preferred technical solution of the present invention, the early warning system at the market level in step S3 is usually responsible for by financial regulatory agencies, exchanges, etc. By monitoring indicators such as market prices, trading volumes, and investor sentiment, market risks and abnormal fluctuations can be discovered in a timely manner to protect the interests of investors and maintain the stable operation of the market. Market fluctuations and anomalies can be discovered in a timely manner by monitoring the prices and trading volumes of financial products such as stocks and bonds.

[0015] As a preferred technical solution of the present invention, the macroeconomic environment risks of the countries or regions in step S3 are usually the responsibility of regulatory agencies to comprehensively monitor and analyze the overall situation of the financial market, timely detect and warn of possible macro risks, where financial institutions conduct risk assessment and monitoring by collecting data such as financial reports and regulatory indicators of financial institutions, and timely discover and handle problem institutions to maintain the stability of the financial system.

[0016] As a preferred technical solution of the present invention, the use of artificial intelligence evaluation in step S4 can effectively detect anti-fraud. By real-time analyzing a large amount of transaction data, AI can quickly identify abnormal transaction behaviors and can continuously optimize the model through learning to improve the accuracy of fraud detection. The abnormal detection algorithm of AI can identify behaviors that do not conform to the normal transaction pattern, such as overly frequent transfers, cross-border transactions, and off-site account logins, so as to issue an alarm in time to prevent fraud from occurring.

[0017] As a preferred technical solution of the present invention, the use of artificial intelligence evaluation in step S4 can monitor market trends in real time through big data analysis and deep learning models and predict potential risk events. Among them, AI can analyze historical market data, news events, and economic indicators to early warn of risks such as possible market crashes and sharp drops in stock prices, and help institutions make preventive decisions in advance.

[0018] As a preferred technical solution of the present invention, the use of artificial intelligence evaluation in step S4 can timely identify potential operational risks by real-time monitoring the system operation process. Through machine learning, AI can detect abnormal behaviors or operational mistakes in the system and take corrective measures before the problem expands. In addition, AI can also predict risk events such as data leakage and hacker attacks in advance through prediction models.

[0019] As a preferred technical solution of the present invention, different stress scenarios are set in step S5, such as rising market interest rates and falling asset prices, to test the vulnerability of enterprises.

[0020] As a preferred technical solution of the present invention, the enterprise risk classification in step S6 can plan the risk levels of all enterprises, classify enterprises according to different risk levels, so as to select the enterprises that need to be inspected according to the relevant risk levels.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] (1) The present invention is an enterprise financial risk early warning and risk detection and identification method based on DTW-RNN. The purpose of the enterprise financial risk early warning set by the present invention is to timely discover and respond to potential financial risks, ensure the financial health and stable operation of the enterprise. By establishing and improving the financial risk early warning system, the enterprise can better cope with various financial risks and guarantee the long-term development of the enterprise.

[0023] (2) The present invention is an enterprise financial risk early warning and risk detection and identification method based on DTW-RNN. The enterprise financial risks set by the present invention can help the enterprise understand its financial status, operating results and cash flow conditions according to the financial ratio analysis, so as to discover possible risk points, identify, detect and analyze the risks in advance, and facilitate timely adjustment.

[0024] (3) The present invention is an enterprise financial risk early warning and risk detection and identification method based on DTW-RNN. The enterprise financial risks set by the present invention are evaluated by means of artificial intelligence. Artificial intelligence can process massive data, discover the laws and trends hidden behind the data, can monitor market trends and transaction data in real time, early warn potential risk events, and help financial institutions adjust strategies in time. Through automated credit assessment and anti-fraud detection, financial institutions can significantly shorten the approval cycle and improve work efficiency. Specific implementation manners

[0025] Next, in combination with specific implementation manners, a further description of the invention is made:

[0026] Example 1

[0027] Example 1 includes the following steps:

[0028] S1. First, obtain the list of enterprises to be detected; for each enterprise to be detected in the enterprise list, extract the characteristic attribute data of the current enterprise to be detected;

[0029] S2. Then, analyze the financial statements of the corresponding enterprise, such as the balance sheet, income statement and cash flow statement, through financial ratio analysis to identify potential financial risks;

[0030] S3. Secondly, pay attention to the volatility and abnormal conditions of the financial market, including the stock market, bond market and foreign exchange market, and pay attention to the macroeconomic environment risks of the country or region, such as inflation, exchange rate fluctuations, market liquidity risks. It is also necessary to understand the risk status of financial institutions and check the financial, liquidity, credit and operating risks that the enterprise may face;

[0031] S4. Conduct a more extensive analysis of the data source through artificial intelligence to establish a more accurate customer credit model. Through machine learning algorithms, financial institutions can better predict the customer default risk, automatically adjust the credit limit, and make accurate credit risk assessments;

[0032] S5. Conduct corresponding stress tests to simulate extremely adverse economic environments or market conditions, and evaluate the enterprise's debt repayment ability and default probability under these circumstances;

[0033] S6. Finally, conduct corresponding risk classification for the enterprise, and divide different levels for different enterprise risks for reference;

[0034] Among them, the use of artificial intelligence evaluation in step S4 can effectively detect fraud. By real-time analyzing a large amount of transaction data, AI can quickly identify abnormal transaction behaviors, and can continuously optimize the model through learning to improve the accuracy of fraud detection. The abnormal detection algorithm of AI can identify behaviors that do not conform to the normal transaction pattern, such as overly frequent transfers, cross-border transactions, and off-site account logins, so as to issue alerts in a timely manner to prevent fraud. The use of artificial intelligence evaluation in step S4 can monitor market trends in real time and predict potential risk events through big data analysis and deep learning models. Among them, AI can analyze historical market data, news events, and economic indicators to early warn of possible market crashes, sharp drops in stock prices and other risks, and help institutions make preventive decisions in advance. The use of artificial intelligence evaluation in step S4 can identify potential operational risks in a timely manner by real-time monitoring the system operation process. Through machine learning, AI can detect abnormal behaviors or operational mistakes in the system and take corrective measures before the problem expands. In addition, AI can also predict risk events such as data leakage and hacker attacks in advance through prediction models.

[0035] In this embodiment, the evaluation method using artificial intelligence in step S4 enables artificial intelligence to process massive data, discover the rules and trends hidden behind the data, can monitor market trends and transaction data in real time, early warn of potential risk events, and help financial institutions adjust strategies in a timely manner. Through automated credit evaluation and anti-fraud detection, financial institutions can significantly shorten the approval cycle and improve work efficiency. The enterprise risk classification in step S6 can plan the risk levels for all enterprises, classify enterprises according to different risk levels, so as to facilitate the selection of enterprises to be inspected according to the relevant risk levels.

[0036] Embodiment 2

[0037] Example 2. This embodiment further elaborates on Example 1. It includes that in step S1, the list of enterprises can conduct statistics on relevant specifications for all enterprises, facilitating the statistical induction of previous-year reports. Subsequently, corresponding reports are extracted for different enterprises for comparison. The financial ratio analysis in step S2 can help enterprises understand their financial status, operating results, and cash flow conditions, thereby discovering potential risk points. The early warning system at the market level in step S3 is usually responsible for by financial regulatory agencies, exchanges, etc. By monitoring indicators such as market prices, trading volumes, and investor sentiment, market risks and abnormal fluctuations are discovered in a timely manner to protect the interests of investors and maintain the stable operation of the market. Market fluctuations and abnormal situations can be discovered in a timely manner by monitoring the prices and trading volumes of financial products such as stocks and bonds. The macroeconomic environment risks of a country or region in step S3 are usually responsible for by regulatory agencies to comprehensively monitor and analyze the overall situation of the financial market and discover and warn of possible macro risks in a timely manner. Among them, financial institutions conduct risk assessment and monitoring by collecting data such as financial reports and regulatory indicators of financial institutions, discover and handle problem institutions in a timely manner to maintain the stability of the financial system.

[0038] In this embodiment, in step S5, different stress scenarios are set through stress tests, such as rising market interest rates and falling asset prices, to test the vulnerability of enterprises.

[0039] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for early warning and risk detection of enterprise financial risks based on DTW-RNN, characterized in that: The steps include: S1. First, obtain a list of enterprises to be tested; for each enterprise to be tested in the list, extract characteristic attribute data of the current enterprise to be tested; S2. Analyze the financial statements of the corresponding enterprises, such as balance sheet, income statement and cash flow statement, through financial ratio analysis to identify potential financial risks; S3. Secondly, pay attention to the volatility and abnormal situations of financial markets, including stock markets, bond markets and foreign exchange markets, and pay attention to the risks of the macroeconomic environment of the country or region, such as inflation, exchange rate fluctuations, and market liquidity risks. It is also necessary to understand the risk status of financial institutions and check the financial, liquidity, credit and operating risks that enterprises may face; S4. Through artificial intelligence, more extensive analysis of data sources can be conducted to establish a more accurate customer credit model. Through machine learning algorithms, financial institutions can better predict customer default risks, automatically adjust credit limits, and make accurate credit risk assessments. S5. Conduct corresponding stress tests to simulate extremely adverse economic environments or market conditions and assess the debt repayment capacity and default probability of enterprises under these circumstances; S6. Finally, the corresponding risk classification is carried out for the enterprises, and different levels are assigned to different enterprise risks for reference.

2. According to the DTW-RNN-based enterprise financial risk early warning and risk detection and identification method of claim 1, it is characterized in that: The enterprise list in step S1 can be used to perform statistics of relevant specifications for all enterprises, which is convenient for statistical summary of reports in previous years, and subsequent extraction of corresponding reports for different enterprises for comparison.

3. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The financial ratio analysis in step S2 can help enterprises understand their financial status, operating results and cash flow, thereby discovering possible risk points.

4. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The market-level early warning system in step S3 is usually responsible for financial regulatory agencies, exchanges, etc., and can promptly detect market risks and abnormal fluctuations by monitoring indicators such as market prices, trading volumes, and investor sentiment to protect investor interests and maintain the stable operation of the market. It can promptly detect market fluctuations and abnormal situations by monitoring the prices and trading volumes of financial products such as stocks and bonds.

5. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The macroeconomic environment risks of the country or region in step S3 are usually the responsibility of the regulatory authorities, which comprehensively monitor and analyze the overall situation of the financial market, timely discover and warn of possible macroeconomic risks. Financial institutions conduct risk assessment and monitoring by collecting financial reports, regulatory indicators and other data from financial institutions, and timely discover and deal with problematic institutions to maintain the stability of the financial system.

6. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The use of artificial intelligence evaluation in step S4 can effectively prevent fraud. By analyzing a large amount of transaction data in real time, AI can quickly identify abnormal transaction behavior and continuously optimize the model through learning to improve the accuracy of fraud detection. AI's anomaly detection algorithm can identify behaviors that are inconsistent with normal transaction patterns, such as too frequent transfers, cross-border transactions, and remote account logins, thereby issuing timely alarms to prevent the occurrence of fraud.

7. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The artificial intelligence assessment used in step S4 can monitor market trends in real time and predict potential risk events through big data analysis and deep learning models, where AI can analyze historical market data, news events and economic indicators, and provide early warning of possible market crashes, stock price plunges and other risks, helping institutions make preventive decisions in advance.

8. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The artificial intelligence assessment used in step S4 can identify potential operational risks in a timely manner by real-time monitoring of the system operation process. Through machine learning, AI can detect abnormal behavior or operational errors in the system and take corrective measures before the problem expands. In addition, AI can also predict risk events such as data leakage and hacker attacks in advance through predictive models.

9. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: In step S5, different stress scenarios are set through stress testing, such as rising market interest rates, falling asset prices, etc., to test the vulnerability of the enterprise.

10. The enterprise financial risk early warning and risk detection and identification method based on DTW-RNN according to claim 1 is characterized in that: The enterprise risk classification in step S6 can plan the risk levels of all enterprises and classify enterprises according to different risk levels, so as to select enterprises that need to be inspected according to relevant risk levels.